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Datasets:
solsticestudioai
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saas-growth-pack

Tasks:
Tabular Classification
Tabular Regression
Time Series Forecasting
Modalities:
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Text
Formats:
csv
Languages:
English
Size:
1K - 10K
Tags:
Synthetic
saas
business-intelligence
analytics
dashboards
startup
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pandas
Polars
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saas-growth-pack / SCHEMA.md
Justin
Initial public release of Solstice SaaS Growth Pack (sample)
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Solstice SaaS Growth Pack — Schema

Goal

A dashboard-ready synthetic SaaS metrics pack. Imports cleanly into any BI tool and immediately supports SaaS growth, acquisition, and retention dashboards — no cleanup, no modeling.

Pack Contents

companies.csv

Grain: company_id

Column Type Description
company_id string Stable primary key for each company
company_name string Human-readable company name
industry string Industry classification
growth_style string Synthetic profile used to drive realistic trends
founded_date date Company founding date
avg_revenue_per_customer decimal Average monthly revenue per active customer
gross_margin_pct decimal Gross margin percentage used in LTV estimates
initial_active_customers integer Starting active customer base

growth_metrics.csv

Grain: date x company_id

Column Type Description
date date Observation date
company_id string Foreign key to companies.csv
company_name string Convenience label for charting
revenue decimal Estimated recognized revenue for the day (≈ MRR / 30.44 with small daily variation)
mrr decimal Monthly recurring revenue estimate
new_customers integer Customers acquired on the day
churned_customers integer Customers lost on the day
active_customers integer Active customer count at day end
cac decimal Customer acquisition cost
ltv decimal Customer lifetime value estimate
marketing_spend decimal Marketing spend for the day
churn_rate decimal Daily churn rate as a share of previous active customers

channel_performance.csv

Grain: date x company_id x channel

Column Type Description
date date Observation date
company_id string Foreign key to companies.csv
company_name string Convenience label for charting
channel string Acquisition channel
impressions integer Channel impressions
clicks integer Channel clicks
conversions integer New customers attributed to the channel
cost decimal Daily channel spend
revenue_generated decimal Revenue attributed to channel conversions
conversion_rate decimal conversions / clicks
click_through_rate decimal clicks / impressions

customer_segments.csv

Grain: company_id x segment

Column Type Description
company_id string Foreign key to companies.csv
company_name string Convenience label for charting
segment string Customer segment (SMB, Mid-Market, Enterprise)
avg_ltv decimal Average LTV for the segment
avg_cac decimal Average CAC for the segment
churn_rate decimal Segment churn rate
avg_revenue decimal Average recurring revenue per customer in the segment

metric_definitions.csv

Grain: metric_name

Column Type Description
metric_name string Name of metric
definition string Human-readable definition
formula string Formula reference
table_name string Source table
grain string Grain where the metric is valid

dashboard_suggestions.csv

Grain: dashboard_name x chart_name

Column Type Description
dashboard_name string Suggested dashboard grouping
chart_name string Suggested chart title
chart_type string Suggested visualization type
primary_table string Main source table
x_axis string Recommended x-axis field
y_axis string Recommended y-axis field(s)
filter_suggestion string Suggested dashboard filters

Join Model

  • companies.company_id = growth_metrics.company_id
  • companies.company_id = channel_performance.company_id
  • companies.company_id = customer_segments.company_id

The dataset is intentionally denormalized with company_name repeated in fact tables so dashboards can still work even if users only import one or two files.

Metric Definitions

revenue

  • Formula: (active_customers * avg_revenue_per_customer) / 30.44
  • Notes: Daily recognized revenue approximation. Summing a full month of revenue reconciles to mrr within ~5%.

mrr

  • Formula: active_customers * avg_revenue_per_customer
  • Notes: Included directly in growth_metrics.csv

cac

  • Formula: marketing_spend / new_customers
  • Notes: Protected from divide-by-zero by generator rules

ltv

  • Formula: (avg_revenue_per_customer * gross_margin_pct) / max(churn_rate, 0.01)
  • Notes: Daily churn rate is floored at 0.01 to avoid unstable LTV spikes on low-churn days.

churn_rate

  • Formula: churned_customers / previous_active_customers

conversion_rate

  • Formula: conversions / clicks

click_through_rate

  • Formula: clicks / impressions

Synthetic Profiles

The generator uses multiple company profiles so the dashboards show realistic differences:

  • steady_plg: strong SEO/content/referral, efficient long-term growth
  • paid_accelerator: aggressive paid acquisition, higher spend and growth
  • enterprise_lumpy: quarter-end deal spikes and lower churn
  • seasonal_b2c: seasonal demand swings
  • churn_recovery: visible churn event followed by recovery
  • capital_infusion: growth acceleration after a mid-period expansion phase

Dashboard Recommendations

SaaS Growth Overview

  • Revenue Over Time
  • MRR and Active Customers

Acquisition Efficiency

  • CAC vs LTV
  • Channel Revenue Contribution

Customer Health

  • New vs Churned Customers (Clustered Column)
  • Churn Rate Over Time (Line)

Segment Economics

  • Segment LTV/CAC (Grouped Bar)
  • Segment Revenue Mix (Stacked Bar)

Import Notes

  • All dates are ISO-8601 (YYYY-MM-DD)
  • Currency values are USD
  • IDs are stable and consistent
  • No null-heavy cleanup is required before dashboarding